Online Methods for Complex Stochastic Optimization Problems

复杂随机优化问题的在线方法

• 批准号: 2585444
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2585444
• 关键词: Online; Methods; Complex; Stochastic; Optimization

My research primarily consists of examining the class of adaptive stepsize algorithms in the context of stochastic optimisation. In recent years, such algorithms have been an integral component to the success of the field of deep learning, allowing previously impractical neural architectures to be trained on huge swathes of data. Adaptive stepsize algorithms for stochastic optimisation extend stochastic gradient descent, a technique for fitting classes of statistical and ML models, to be more robust and efficient by utilising additional data whilst training. Until recently, however, relatively little was known about the practical performance of these adaptive stepsize algorithms with many important properties, such as stability and probabilistic convergence, being undetermined. Recent work has been undertaken to try and isolate these properties and the conditions in which they hold - The research I am undertaking will aim to widen the conditions in which we know that these properties hold, looking toward weakening the assumptions in the standard setting (also strengthening the properties themselves), and also looking at how the algorithms behave in condition of dependent data, mostly focusing on markovian dynamics. This work will facilitate increased confidence in the performance of adaptive step size algorithms which will become increasingly more important given the growing prevalence of machine learning techniques using them being relied upon in critical infrastructure. This work will also increase the range of settings in which adaptive stepsize algorithms can be applied such as training reinforcement learning algorithms which often entail Markovian conditions.

我的研究主要包括在随机优化的背景下检查自适应步骤算法的类别。近年来，这种算法一直是深度学习领域成功的组成部分，从而使以前不切实际的神经体系结构可以接受大量数据的培训。自适应步骤算法用于随机优化扩展了随机梯度下降，这是一种拟合统计和ML模型类别的技术，通过在训练中利用其他数据，以更加稳健和有效。然而，直到最近，对于这些自适应步骤算法的实践性能却相对较少，尚不确定具有许多重要属性，例如稳定性和概率收敛。最近已经开展了尝试隔离这些特性及其所拥有的条件的工作 - 我正在进行的研究将旨在扩大我们知道这些特性所具有的条件，希望削弱标准环境中的假设（也可以增强属性本身），并着眼于算法在依赖数据中如何侧重于标记动力学，并在依赖算法的状态。这项工作将促进对自适应步长算法的性能的增强信心，因为使用它们在关键基础架构中依赖于机器学习技术的越来越多的流行率将变得越来越重要。这项工作还将增加可以应用自适应步骤算法的设置范围，例如训练强化学习算法，这些学习算法通常需要马尔可夫条件。